# Test for feature dependencies in time series modelling

I have time-series data that track event occurrence in 3 locations. Here's a sample:

               Count     Total
Location       A  B  C
Date
2018-06-22     0  1  1     2
2018-06-23     2  1  0     3
2018-06-24     0  0  1     1
2018-06-25     2  2  1     5
2018-06-26     0  3  1     4


I would like to use the data to predict the total number of event occurrences at a given date in the future. How do I test if an event happening in one location has an impact on events happening in another location (dependency)? I believe that if an event happening in locations B and C are dependant, I should sum the 2 columns together as 1 feature in my model.

• A $$\chi$$-square test would tell you whether there is a significant difference between an observed variable (e.g. count in one location) and an expected variable (count in the other location). In other words, it can tell you whether the variables are independent or not.
• The conditional probability $$p(A|B)$$ of a variable A given the other variable B tells you how likely the event A is assuming the event B happens. $$A$$ and $$B$$ are independent if $$p(A|B)=p(A)$$ (note that it's unlikely to be exactly equal in the case of a real sample).